(i) Regularization model: Motivated by prior work on nonlocal regularization, we introduce a regularizer for remote sensing images that can exploit self-similar structures such as textures using image patches. However, unlike existing nonlocal regularizers [1]}, [2]}, [3]}, our regularizer is convex. We note that the convex nonlocal regularizer in [4]} is designed to capture pixel-based variations. In contrast, we use patches for penalizing variations jointly across all bands. Moreover, unlike VTV and local regularizers [5]}, [6]}, [7]}, where variations along just horizontal and vertical directions are considered, we use a wider search window in terms of nonlocality and directionality. Finally, since in fusion we have a high resolution image of the same scene, we use this as a guide for computing weights, which are incorporated in the regularizer.
